[STAKEME]_zkML_Linear_Regression.ipynb

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      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "##prepare"
      ],
      "metadata": {
        "id": "RNcc10WO61Yy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "nqPZvhM_gjtP",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "e20b1c1b-35e7-497b-8824-043816360c78"
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          "text": [
            "--2023-05-14 20:43:54--  https://github.com/AleoHQ/leo/releases/download/v1.7.0/leo-v1.7.0-x86_64-unknown-linux-musl.zip\n",
            "Resolving github.com (github.com)... 20.205.243.166\n",
            "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/246203444/fbd82088-a0ae-4081-9606-8b4a8f013455?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230514%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230514T204354Z&X-Amz-Expires=300&X-Amz-Signature=a0efb448a801913ad2a58b8719761dcd18e19003a6aaeae33c73d87d3859bccd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=246203444&response-content-disposition=attachment%3B%20filename%3Dleo-v1.7.0-x86_64-unknown-linux-musl.zip&response-content-type=application%2Foctet-stream [following]\n",
            "--2023-05-14 20:43:54--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/246203444/fbd82088-a0ae-4081-9606-8b4a8f013455?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230514%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230514T204354Z&X-Amz-Expires=300&X-Amz-Signature=a0efb448a801913ad2a58b8719761dcd18e19003a6aaeae33c73d87d3859bccd&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=246203444&response-content-disposition=attachment%3B%20filename%3Dleo-v1.7.0-x86_64-unknown-linux-musl.zip&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 12827158 (12M) [application/octet-stream]\n",
            "Saving to: ‘leo-v1.7.0-x86_64-unknown-linux-musl.zip’\n",
            "\n",
            "leo-v1.7.0-x86_64-u 100%[===================>]  12.23M  9.85MB/s    in 1.2s    \n",
            "\n",
            "2023-05-14 20:43:56 (9.85 MB/s) - ‘leo-v1.7.0-x86_64-unknown-linux-musl.zip’ saved [12827158/12827158]\n",
            "\n",
            "Archive:  leo-v1.7.0-x86_64-unknown-linux-musl.zip\n",
            "  inflating: leo                     \n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/AleoHQ/leo/releases/download/v1.7.0/leo-v1.7.0-x86_64-unknown-linux-musl.zip\n",
        "!unzip leo-v1.7.0-x86_64-unknown-linux-musl.zip\n",
        "!rm -rf leo-v1.7.0-x86_64-unknown-linux-musl.zip"
      ]
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    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.githubusercontent.com/icodragon/63ede7ff4478c680049aa353d1cefb11/raw/3985fb403e3c0839000c62396b49116be7d67692/BostonHousing.csv"
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-14 20:43:57--  https://gist.githubusercontent.com/icodragon/63ede7ff4478c680049aa353d1cefb11/raw/3985fb403e3c0839000c62396b49116be7d67692/BostonHousing.csv\n",
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            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 33167 (32K) [text/plain]\n",
            "Saving to: ‘BostonHousing.csv’\n",
            "\n",
            "\rBostonHousing.csv     0%[                    ]       0  --.-KB/s               \rBostonHousing.csv   100%[===================>]  32.39K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-05-14 20:43:57 (107 MB/s) - ‘BostonHousing.csv’ saved [33167/33167]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/stakemepro/aleo-zkml-initiative-1.git && mv aleo-zkml-initiative-1/interp_leo interp_leo && rm -rf aleo-zkml-initiative-1"
      ],
      "metadata": {
        "id": "KFFq2GjI64f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37bedcba-ddcd-4229-c6d6-5f2a648542ac"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'aleo-zkml-initiative-1'...\n",
            "remote: Enumerating objects: 51, done.\u001b[K\n",
            "remote: Counting objects: 100% (51/51), done.\u001b[K\n",
            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 51 (delta 23), reused 37 (delta 12), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (51/51), 7.64 KiB | 489.00 KiB/s, done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Two linear regression algorithms are provided. One implementation purely in Python, the second using Aleo**"
      ],
      "metadata": {
        "id": "uiT21gvI6NeX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  First Linear Regression Implementation Without Aleo (Floating Point)"
      ],
      "metadata": {
        "id": "1fsKE4sy6Ypj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "def load_data():\n",
        "    data = pd.read_csv('./BostonHousing.csv')\n",
        "    X = data.drop('medv', axis=1)\n",
        "    y = data['medv']\n",
        "    X = np.round(X).astype(int)\n",
        "    y = np.round(y).astype(int)\n",
        "    \n",
        "    return X, y \n",
        "\n",
        "X, y = load_data()\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "cvyyIebusiz9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_leo = X_train.values.tolist()\n",
        "y_train_leo = y_train.values.tolist()\n",
        "\n",
        "print(X_train_leo)\n",
        "print(y_train_leo)"
      ],
      "metadata": {
        "id": "IYdsFuDSivoQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28ed3b99-1a11-4c56-925c-45697440304f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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281, 19, 397, 5], [0, 0, 11, 0, 6, 22, 4, 4, 277, 19, 397, 11], [2, 0, 8, 0, 6, 97, 4, 4, 307, 21, 248, 20], [8, 0, 18, 0, 6, 86, 2, 24, 666, 20, 83, 18], [1, 20, 4, 0, 7, 94, 2, 5, 264, 13, 393, 11], [38, 0, 18, 0, 6, 79, 2, 24, 666, 20, 19, 15], [4, 0, 18, 0, 5, 91, 2, 24, 666, 20, 316, 14], [0, 0, 3, 0, 6, 74, 3, 2, 270, 18, 394, 8], [0, 0, 5, 0, 6, 45, 5, 5, 224, 20, 397, 10], [14, 0, 18, 0, 6, 88, 2, 24, 666, 20, 383, 13], [0, 21, 6, 0, 7, 21, 7, 4, 243, 17, 397, 5], [0, 0, 4, 0, 6, 48, 8, 3, 352, 19, 386, 11], [1, 20, 4, 0, 7, 92, 2, 5, 264, 13, 388, 8], [0, 30, 5, 0, 6, 65, 6, 6, 300, 17, 395, 12], [1, 0, 8, 0, 6, 62, 5, 4, 307, 21, 397, 8], [0, 0, 10, 0, 6, 78, 4, 4, 304, 18, 396, 12], [0, 0, 3, 0, 7, 86, 3, 2, 270, 18, 397, 6], [23, 0, 18, 0, 5, 90, 2, 24, 666, 20, 397, 32], [0, 20, 7, 1, 8, 52, 4, 3, 223, 19, 391, 7], [2, 0, 20, 0, 8, 96, 2, 5, 403, 15, 369, 4], [0, 0, 3, 0, 6, 26, 5, 4, 430, 17, 382, 10], [0, 0, 10, 0, 6, 65, 2, 6, 391, 19, 397, 13], [1, 0, 22, 0, 6, 98, 2, 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3, 4, 304, 18, 393, 8], [7, 0, 18, 0, 6, 84, 3, 24, 666, 20, 397, 15], [1, 0, 20, 1, 6, 93, 2, 5, 403, 15, 339, 6], [0, 40, 6, 1, 7, 49, 5, 4, 254, 18, 389, 6], [10, 0, 18, 0, 6, 100, 2, 24, 666, 20, 387, 19], [0, 45, 3, 0, 7, 31, 6, 5, 398, 15, 390, 5], [17, 0, 18, 0, 5, 98, 1, 24, 666, 20, 397, 31], [0, 0, 14, 0, 6, 18, 6, 4, 289, 16, 397, 9], [29, 0, 18, 0, 5, 100, 2, 24, 666, 20, 211, 20], [0, 55, 4, 0, 7, 56, 6, 5, 370, 18, 397, 7], [1, 20, 4, 0, 9, 87, 2, 5, 264, 13, 390, 5], [0, 0, 10, 0, 7, 82, 3, 4, 304, 18, 397, 5], [3, 0, 20, 0, 6, 95, 2, 5, 403, 15, 352, 21], [0, 0, 22, 0, 6, 99, 2, 4, 437, 21, 395, 13], [1, 0, 20, 0, 5, 100, 2, 5, 403, 15, 342, 13], [4, 0, 18, 0, 6, 81, 3, 24, 666, 20, 391, 13], [0, 0, 3, 0, 7, 62, 3, 2, 276, 18, 397, 6], [8, 0, 18, 1, 6, 90, 1, 24, 666, 20, 348, 9], [0, 0, 4, 0, 6, 57, 4, 3, 247, 18, 395, 8], [8, 0, 18, 0, 6, 84, 3, 24, 666, 20, 272, 16], [0, 0, 3, 0, 6, 32, 6, 4, 430, 17, 369, 9], [0, 0, 11, 0, 5, 10, 4, 4, 277, 19, 349, 30], [4, 0, 18, 0, 6, 85, 3, 24, 666, 20, 22, 17], [0, 0, 26, 0, 6, 84, 2, 2, 188, 19, 378, 14], [0, 0, 12, 0, 6, 81, 3, 1, 273, 21, 397, 8], [1, 0, 8, 0, 6, 98, 4, 4, 307, 21, 377, 21], [0, 45, 3, 0, 7, 29, 5, 5, 398, 15, 383, 5], [0, 0, 11, 0, 6, 18, 5, 4, 305, 19, 377, 10], [0, 0, 9, 0, 6, 92, 2, 5, 384, 21, 396, 19], [0, 20, 7, 0, 6, 42, 4, 3, 223, 19, 389, 13], [0, 80, 2, 0, 7, 30, 8, 4, 280, 17, 391, 6], [11, 0, 18, 0, 7, 95, 2, 24, 666, 20, 110, 23], [0, 0, 9, 0, 6, 85, 3, 5, 384, 21, 71, 11]]\n",
            "[12, 20, 19, 13, 18, 25, 21, 25, 9, 28, 21, 36, 32, 12, 40, 14, 22, 24, 18, 24, 9, 19, 25, 20, 23, 38, 16, 45, 16, 23, 14, 19, 18, 16, 21, 32, 29, 16, 18, 22, 19, 19, 8, 21, 17, 17, 14, 50, 14, 13, 29, 21, 19, 23, 19, 25, 33, 5, 30, 19, 22, 23, 23, 21, 49, 15, 17, 27, 20, 20, 21, 41, 23, 20, 18, 29, 36, 24, 12, 14, 12, 18, 33, 27, 13, 14, 50, 22, 20, 24, 18, 13, 6, 31, 26, 19, 17, 14, 23, 15, 28, 36, 23, 24, 25, 50, 35, 32, 24, 22, 14, 43, 19, 32, 26, 22, 22, 8, 47, 43, 32, 10, 17, 20, 33, 18, 50, 20, 23, 13, 20, 23, 29, 31, 23, 22, 24, 33, 24, 22, 25, 8, 26, 23, 15, 9, 19, 24, 25, 20, 24, 13, 29, 27, 35, 13, 16, 12, 15, 11, 25, 17, 8, 21, 16, 23, 32, 39, 30, 20, 32, 42, 24, 21, 22, 18, 15, 6, 20, 21, 28, 30, 21, 23, 14, 12, 37, 17, 10, 23, 23, 20, 22, 50, 8, 19, 37, 16, 16, 22, 21, 14, 48, 24, 23, 17, 30, 36, 42, 18, 22, 19, 45, 12, 19, 16, 22, 7, 20, 14, 13, 18, 23, 21, 23, 24, 50, 27, 22, 50, 8, 23, 22, 19, 18, 17, 13, 12, 27, 22, 28, 20, 22, 14, 11, 30, 27, 10, 23, 24, 46, 22, 8, 36, 44, 18, 28, 38, 14, 28, 10, 19, 44, 28, 25, 16, 17, 13, 50, 22, 33, 15, 15, 14, 24, 34, 22, 50, 10, 13, 22, 18, 18, 25, 16, 23, 20, 33, 25, 18, 13, 35, 10, 20, 28, 23, 35, 13, 22, 18, 25, 22, 22, 29, 20, 25, 24, 21, 33, 23, 21, 24, 50, 25, 29, 7, 37, 20, 30, 20, 23, 12, 22, 15, 15, 19, 8, 28, 23, 14, 14, 31, 11, 22, 22, 19, 21, 25, 18, 36, 20, 20, 16, 24, 7, 35, 50, 19, 21, 16, 33, 19, 21, 24, 19, 17, 20, 18, 23, 12, 35, 21, 20, 32, 22, 23, 14, 31, 24, 30, 20, 22, 20, 27, 33, 15, 30, 7, 24, 16, 24, 50, 23, 15, 19, 20, 23, 33, 50, 22, 15, 20, 24, 19, 20, 12, 14, 30, 22, 20, 21, 24, 13, 19]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "weights = model.coef_\n",
        "bias = model.intercept_"
      ],
      "metadata": {
        "id": "G6I_VNP9exRK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "id": "4Nx6hHEd8fZN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "6a8a3275-d3c3-47a7-d977-829bbb2dcff8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     crim  zn  indus  chas  rm  age  dis  rad  tax  ptratio    b  lstat\n",
              "173     0   0      4     0   6   84    3    5  296       17  396      9\n",
              "274     0  40      6     1   7   33    4    4  254       18  397      4\n",
              "491     0   0     28     0   6   99    2    4  711       20  390     18\n",
              "72      0   0     11     0   6    8    5    4  305       19  391      6\n",
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        "def linear_regression_predict(weights, features, bias):\n",
        "    prediction = 0\n",
        "    for i in range(len(weights)):\n",
        "        prediction += weights[i] * features[i]\n",
        "    prediction += bias\n",
        "    return prediction\n",
        "\n",
        "y_pred = [linear_regression_predict(weights, x, bias) for x in X_test.values.tolist()]\n",
        "y_pred"
      ],
      "metadata": {
        "id": "gVInAiMFQR2x",
        "colab": {
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        },
        "outputId": "b340f9f4-085b-494f-9bb0-57b2fde3eda0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "def rmse(y_true, y_pred):\n",
        "    '''\n",
        "    Compute Root Mean Square Percentage Error between two arrays.\n",
        "    '''\n",
        "    loss = np.sqrt(np.mean(np.square(((y_true - y_pred) / y_true)), axis=0))\n",
        "\n",
        "    return loss"
      ],
      "metadata": {
        "id": "22T7utyn34EP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "print('🔥 RMSE error:', rmse(np.array(y_test), np.array(y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29gP-z-aQT7G",
        "outputId": "5817bf61-4610-4d77-9c4b-6da2f29d924f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔥 RMSE error: 0.28489684189503395\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Second Linear Regression Implementation With Aleo (Floating Point)"
      ],
      "metadata": {
        "id": "J2oFfmnZ6Tb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Utils function for generate aleo code linear_regression\n",
        "def generate_aleo_code(weights, integer_type='i32'):\n",
        "    num_features = len(weights)\n",
        "    function_str = \"transition linear_regression_predict(\"\n",
        "    # Add weight and feature arguments\n",
        "    for i in range(num_features):\n",
        "        function_str += f\"weight{i}: {integer_type}, feature{i}: {integer_type}, \"\n",
        "    # Add bias argument\n",
        "    function_str += f\"bias: {integer_type}) -> {integer_type} {{\\n\"\n",
        "    \n",
        "    # Add body of function\n",
        "    function_str += f\"let prediction: {integer_type} = \"\n",
        "    for i in range(num_features):\n",
        "        function_str += f\"weight{i} * feature{i} + \"\n",
        "    function_str += \"bias;\\n\"\n",
        "    \n",
        "    function_str += \"return prediction;\\n\"\n",
        "    function_str += \"}\"\n",
        "    \n",
        "    return function_str\n"
      ],
      "metadata": {
        "id": "PrGIWGLiEYIF"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generate new project aleo\n",
        "!./leo new linear_regression"
      ],
      "metadata": {
        "id": "NsfXkLoNNA5O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "854ff806-67d0-4532-954b-a1de25e27ade"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;36m       Leo\u001b[0m ✅ Created an Aleo program '\u001b[1mlinear_regression\u001b[0m' \u001b[2m(in \"/content/linear_regression\")\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "def load_data():\n",
        "    data = pd.read_csv('./BostonHousing.csv')\n",
        "    X = data.drop('medv', axis=1)\n",
        "    y = data['medv']\n",
        "    X = np.round(X).astype(int)\n",
        "    y = np.round(y).astype(int)\n",
        "\n",
        "    return X, y\n",
        "\n",
        "\n",
        "def float_to_int(float_number, scaling_factor=1000):\n",
        "    return round(float_number * scaling_factor)\n",
        "\n",
        "\n",
        "def int_to_float(int_number, scaling_factor=1000):\n",
        "    return int_number / scaling_factor\n",
        "\n",
        "\n",
        "X, y = load_data()\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "X_train_leo = X_train.applymap(float_to_int).values.tolist()\n",
        "y_train_leo = y_train.apply(float_to_int).values.tolist()\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "weights = model.coef_\n",
        "bias = model.intercept_\n",
        "\n",
        "# Scale weights and bias\n",
        "weights_scaled = [int(w) for w in weights]\n",
        "bias_scaled = int(bias)\n",
        "\n",
        "def linear_regression_predict(weights, features, bias):\n",
        "    prediction = 0\n",
        "    for i in range(len(weights)):\n",
        "        prediction += weights[i] * features[i]\n",
        "    prediction += bias\n",
        "    return prediction\n",
        "\n",
        "zero_weight_columns = X.columns[np.array(weights_scaled) == 0]\n",
        "\n",
        "# Scale features for prediction\n",
        "X_test = X_test.drop(zero_weight_columns, axis=1)\n",
        "X_test_scaled = X_test.applymap(float_to_int).values.tolist()\n",
        "weights_scaled = [weight for weight in weights_scaled if weight != 0]"
      ],
      "metadata": {
        "id": "cTOKxpgaUtRm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fill code in main.leo\n",
        "\n",
        "content_program_aleo = \"program linear_regression.aleo { %code% }\".replace('%code%', generate_aleo_code(weights_scaled))\n",
        "with open('linear_regression/src/main.leo', 'w') as file:\n",
        "    file.write(content_program_aleo)"
      ],
      "metadata": {
        "id": "_LasKdBwNjtY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "from interp_leo.leo_uitls import convert_from_leo_type\n",
        "from interp_leo.leo_program import LeoProgram\n",
        "\n",
        "leo_program = LeoProgram(path=os.getcwd() + '/linear_regression')\n",
        "\n",
        "y_pred_scaled = []\n",
        "iter = 1\n",
        "for x in X_test_scaled:\n",
        "  print('iteration', f\"{iter}/{len(X_test_scaled)}\")\n",
        "  value = leo_program.linear_regression_predict(*([val for pair in zip(weights_scaled, x) for val in pair] + [bias_scaled]))\n",
        "  y_pred_scaled.append(convert_from_leo_type(value))\n",
        "  iter += 1\n",
        "\n",
        "y_pred_scaled"
      ],
      "metadata": {
        "id": "EHkv7S7HNmBv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d70f3459-c3bf-4052-9034-e01776f18ebe"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iteration 1/102\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[15022]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict using scaled weights, features, and bias, and convert result back to float\n",
        "# y_pred_scaled = [linear_regression_predict(weights_scaled, x, bias_scaled) for x in X_test_scaled]\n",
        "y_pred = np.array(y_pred_scaled)"
      ],
      "metadata": {
        "id": "jt7VWXQqrIu_"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rmse(y_true, y_pred):\n",
        "    '''\n",
        "    Compute Root Mean Square Percentage Error between two arrays.\n",
        "    '''\n",
        "    loss = np.sqrt(np.mean(np.square(((y_true - y_pred) / y_true)), axis=0))\n",
        "\n",
        "    return loss"
      ],
      "metadata": {
        "id": "glLLvkMk4NFA"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "y_test = np.array(y_test.apply(float_to_int))\n",
        "print('🔥 RMSE error:', rmse(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_zohI5Vys2p",
        "outputId": "099aef44-6d34-49c9-ea03-138c2dec269b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔥 RMSE error: 0.42756238389862905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:** From the two RMSE values, we can see that the result due to the floating point may be slightly different and there is more probability of errors.\n",
        "But, work has also been done on generating aleo code for calling linear_regression_predict"
      ],
      "metadata": {
        "id": "h2n-D0Py6DpQ"
      }
    }
  ]
}